Situated Algorithmic Thinking: Preparing the Future Computing Workforce for Ethical Decision-Making through Interactive Case Studies

该项目旨在通过研究本科生如何培养道德决策技能来服务于国家利益。它将特别关注算法思维背景下的这些技能。数学思维通过识别一步一步的指令来解决问题，这些指令可以解决原始问题，也可以一次又一次地用于解决相关问题。数学思维是计算思维、信息技术和计算的基础，所有这些都对生活的许多方面产生了积极的影响。 然而，人们越来越担心基于算法的技术可能会对个人和社会造成伤害。 一个担忧是运行软件的算法可能会产生意想不到的结果，包括产生有偏见的结果。 例如，设计用于评估工程工作候选人的招聘算法可能偏向于雇用男性工程师，因为有关成功工程师的数据主要是关于成功的男性工程师。 因此，将道德决策纳入计算机科学课程非常重要，以便未来的程序员能够意识到并减轻意外的算法结果。为了实现这一目标，该项目将开发，实施和测试六个交互式案例研究，让本科生参与算法思维和算法设计的伦理方面。案例研究将使学生从不同的角度思考算法决策问题。这一经验预计将促进学生在算法思维的背景下的道德考虑的理解。互动式案例研究将适用于其他课程、独立讲习班和其他机构。使用算法思维来设计和开发技术的能力是未来劳动力教育的核心问题。在社会层面上，算法决策的挑战在于，算法可能（1）由于用于训练不同模型的基础数据的特征而存在偏见;（2）不负责任，因为如果算法被滥用或出错，没有机制对其进行审计或纠正;（3）误解算法驱动的决策如何影响社会正义或道德问题。鉴于算法的重要性及其复杂性，学生学习道德决策作为计算机科学教育的一部分至关重要。基于学习的情境认知范式，该项目将开发交互式案例研究，为学生提供思考算法设计问题的机会。从不同的角度和领域参与材料将使学生能够在算法开发的背景下对道德的重要性有一个很好的理解。该项目的研究部分将使用交互式案例研究来衡量透视理解的发展。该研究将使用混合方法，包括分析学生生成的工件，如概念图和分析学生的解释，使用编码策略来记录重要的趋势和结果。该项目得到了NSF改善本科STEM教育计划的支持：教育和人力资源，该计划支持研究和开发项目，以提高所有学生STEM教育的有效性。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project aims to serve the national interest by studying how undergraduate students develop ethical decision-making skills. It will specifically focus on these skills in the context of algorithmic thinking. Algorithmic thinking solves problems by identifying step-by-step instructions that can solve the original problem as well as be used again and again to solve related problems. Algorithmic thinking is a foundation of computational thinking, information technology, and computing, all of which have had positive effects on many aspects of life. However, there is increasing concern about how algorithm-based technology may harm individuals and society. One concern is the potential for the algorithms that run software to have unintended outcomes, including production of biased results. For example, a hiring algorithm designed to evaluate candidates for an engineering job may be biased toward hiring male engineers because the data about successful engineers is mostly about successful male engineers. Therefore, it will be important to integrate ethical decision making into the computer science curriculum so that future programmers are aware of and can mitigate unintended algorithmic outcomes. Toward this goal, the project will develop, implement, and test six interactive case studies to engage undergraduate students in the ethical aspects of algorithmic thinking and algorithm design. The case studies will enable students to think through issues of algorithmic decision making from different perspectives. This experience is expected to promote student understanding of ethical considerations in the context of algorithmic thinking. The interactive case studies will be adaptable for use in other courses, in standalone workshops, and at other institutions. The ability to use algorithmic thinking to design and develop technology is a core concern for education of the future workforce. The challenge with algorithmic decision-making at a societal level is that algorithms can be (1) biased due to the characteristics of the underlying data used to train different models; (2) unaccountable in that there is no mechanism to audit them or for redress if an algorithm is misused or makes an error; and (3) misunderstood regarding how algorithm-driven decisions impact social justice or ethical issues. Given the importance of algorithms and their complexity, it is critical that students learn ethical decision-making as part of their computer science education. Building on the situated cognition paradigm of learning, this project will develop interactive case studies that provide students the opportunity to think through issues of algorithmic design. Engaging with the material from different perspectives and domains will allow students to develop a well-grounded understanding of the importance of ethics in the context of algorithm development. The research component of the project will measure how perspectival understanding develops using interactive case studies. The research study will use a mixed methods approach involving analysis of student-generated artifacts such as concept maps and analysis of student explanations, using coding strategies to document important trends and outcomes. This project is supported by the NSF Improving Undergraduate STEM Education Program: Education and Human Resources, which supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.